Cooperative target execution system for unmanned aerial vehicle networks

ABSTRACT

An integrated decision making and communication system includes a memory to store a list of resources necessary to execute a mission; a transceiver to send and receive data between communicatively linked devices; and a processor to identify a set of available resources capable of executing the mission based on the data received from the devices; compare the list of resources necessary to execute the mission from the memory with the set of available resources; and identify a combination of the devices to execute the mission based on the comparison of the list of resources necessary to execute the mission and the set of available resources.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims the benefit of U.S. Provisional PatentApplication No. 62/642,631 filed on Mar. 14, 2018, which is incorporatedherein by reference in its entirety.

GOVERNMENT INTEREST

The invention described herein may be manufactured and used by or forthe Government of the United States for all government purposes withoutthe payment of any royalty.

BACKGROUND Field of the Invention

The embodiments herein generally relate to coalition formationtechniques, and more particularly to target detection and subsequenttask completion in a coalition of devices.

Background of the Invention

Recent advancements in communication and computation systems allowdeployment of large teams of Unmanned Aerial Vehicles (UAVs) tocooperatively accomplish complex missions that often cannot be performedby a single. Several features including task coordination and reliablecommunications are typically required to enable interoperability withinthe heterogeneous airborne networks, particularly for autonomousoperations and providing on-board data processing during the mission.These heterogeneous autonomous vehicle systems could provide a greatflexibility to complete compound tasks which are distributed in time andspace.

The task allocation problem in multi-agent systems is defined as theprocess of allocating a set of tasks to groups of agents to ensuretimely and efficient task completions, noting the individualcapabilities of the agents. Previous solutions to the complex problem oftask assignment have involved using different approaches including mixedinteger linear programming, dynamic network flow optimization,market-based strategy, finite state machine, and multiple-choiceknapsack problem. While in the majority of the aforementioned solutions,the tasks are centrally assigned to the agents by a base station thathas complete knowledge about the tasks and often the agents'capabilities; in many dynamic systems, tasks may appear at unpredictablelocations and times (e.g., target detection in military settings, aswell as search and rescue operations).

Hence, a priori knowledge about these tasks is not always available tothe base station. Even when such centralized task allocation techniquesexist, they often require computationally intensive solutions even inhomogeneous networks and are not easily scalable to systems with a largenumber of tasks or agents. Noting the computation and communicationcapabilities of modern devices, the agents can be considered as smartentities with decision-making capabilities. Such cognitive capabilitymay facilitate the implementation of distributed task allocationmechanisms by allowing the agents to observe the environment and monitorthe operation of other agents and properly respond to the observedsituations.

Coalition formation game is a class of games, in which the playerscooperate with each other by forming various sub-groups calledcoalitions. This class of games has been recently used in variousapplications such as task assignment in multi-agent systems, andcommunication networks. In conventional solutions of coalition formationfor task allocation, the objective is to enhance the efficiency of theformed groups in task performance noting the different capabilitiesavailable at the coalition members. Therefore, the agents often considera solution to be optimal when it maximizes the total utilities of thegroup in executing the existing tasks with minimum resources. In thesesolutions, it is assumed that all the agents are fully trustable, andthey are obligated to cooperate with one another by utilizing theirinitially claimed resources to complete the tasks. However, thisassumption is far too optimistic since cooperation is not an inherentcharacteristic of cognitive but potentially self-interested agents.Accordingly, a new solution to cooperative coalition formation isdesirable.

BRIEF SUMMARY OF THE INVENTION

In view of the foregoing, an embodiment herein provides an integrateddecision making and communication system comprising a memory to store alist of resources necessary to execute a mission; a transceiver to sendand receive data between communicatively linked devices; and a processorto identify a set of available resources capable of executing themission based on the data received from the devices; compare the list ofresources necessary to execute the mission from the memory with the setof available resources; and identify a combination of the devices toexecute the mission based on the comparison of the list of resourcesnecessary to execute the mission and the set of available resources.

The processor may instruct the transceiver to send messages to theidentified combination of the devices to execute the mission. The systemmay further comprise a first device containing the memory, thetransceiver, and the processor to autonomously store, transceive, andprocess the data without control by a centralized base station; and aplurality of other functionally similar devices communicatively linkedto the first device. The processor of the first device groups theplurality of other functionally similar devices based on possiblecombinations of devices comprising resources capable of executing themission. The processor of the first device selects the group of theplurality of other functionally similar devices comprising a mostefficient utilization of the available resources to execute the missionin a predetermined period of time.

The transceiver of the first device may transmit a proposal to theplurality of other functionally similar devices to request the pluralityof other functionally similar devices to combine their availableresources to execute the mission; and receives responses from theplurality of other functionally similar devices of whether to they agreeto utilize their available resources to execute the mission. The devicesmay comprise unmanned aerial vehicles (UAVs).

Another embodiment provides a machine-readable storage medium comprisingcomputer-executable instructions that when executed cause a processor ofa first UAV to determine resources used to execute a mission; generate aset of available resources capable of executing the mission from a groupof UAVs in communication with the first UAV; compare the resources usedfor executing the mission with the set of available resources; andidentify a combination of the UAVs to assist in executing the missionbased on the comparison of the resources used for executing the missionand the set of available resources.

In the machine-readable storage medium, wherein the instructions, whenexecuted, further cause the processor to identify the group of UAVs; andtransmit a request to the group of UAVs to send to the first UAV theavailable resources for each of the UAVs in order to generate the set ofavailable resources capable of executing the mission. In themachine-readable storage medium, wherein the instructions, whenexecuted, further cause the processor to generate the set of availableresources capable of executing the mission based on an assessment ofwhich combination of the available resources from the group of UAVs bestmaximizes an efficiency in executing the mission.

In the machine-readable storage medium, wherein the instructions, whenexecuted, further cause the processor to filter out any UAVs from thegroup of UAVs based on a past performance of executing other missions.In the machine-readable storage medium of claim 8, wherein theinstructions, when executed, further cause the processor to filter outany UAVs from the group of UAVs based on a real-time assessment of aperformance of any of the UAVs utilization of its available resources inexecuting the mission.

In the machine-readable storage medium, wherein the instructions, whenexecuted, further cause the processor to filter out any UAVs from thegroup of UAVs that decline to offer available resources in executing themission. In the machine-readable storage medium, wherein theinstructions, when executed, further cause the processor to select thecombination of the UAVs to assist in executing the mission; and transmita coalition message to the selected UAVs to execute the mission. In themachine-readable storage medium, wherein the instructions, whenexecuted, further cause the processor to generate the set of availableresources capable of executing the mission based on assigning apredetermined value to each of the available resources provided by eachof the UAVs; and determine which combination of UAVs corresponds to thepredetermined value of the available resources to complete the missionwithout overspending the available resources.

Another embodiment provides a method of autonomously assessing acooperative performance of a task, the method comprising operating afirst UAV to search for a target; upon locating the target, assessing arequirement for the first UAV to perform the task relating to thetarget; transmitting a first electronic message to other UAVs capable ofassisting the first UAV in performing the task; receiving at least onesecond electronic message from the other UAVs identifying availableresources from each of the other UAVs to perform the task; anddetermining which combination of the other UAVs is best capable ofassisting the first UAV in performing the task based on a predefinedcriterion.

The method may further comprise selecting the combination of the otherUAVs that is best capable of assisting the first UAV in performing thetask; and transmitting a third electronic message to the other UAVsrequesting the selected combination of the other UAVs to assist thefirst UAV in performing the task. The method may further compriseperforming the task using the first UAV with the selected combination ofthe other UAVs. The method may further comprise selecting thecombination of the other UAVs based on any of a time required to performthe task, resource allocation capable of being provided by the otherUAVs, a trust factor associated with the other UAVs, and a likelihood ofperformance of the task based on the available resources from each ofthe other UAVs. Each of the other UAVs may comprise a differentavailable resource to assist the first UAV in performing the task.

These and other aspects of the embodiments herein will be betterappreciated and understood when considered in conjunction with thefollowing description and the accompanying drawings. It should beunderstood, however, that the following descriptions, while indicatingpreferred embodiments and numerous specific details thereof, are givenby way of illustration and not of limitation. Many changes andmodifications may be made within the scope of the embodiments hereinwithout departing from the spirit thereof, and the embodiments hereininclude all such modifications.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein will be better understood from the followingdetailed description with reference to the drawings, in which:

FIG. 1 is a block diagram illustrating an integrated decision making andcommunication system, according to an embodiment herein;

FIG. 2 is a block diagram illustrating transmission of messages from thetransceiver of the integrated decision making and communication systemof FIG. 1, according to an embodiment herein;

FIG. 3 is a block diagram illustrating multiple devices communicativelylinked together in the integrated decision making and communicationsystem of FIG. 1 and without a controlling base station, according to anembodiment herein;

FIG. 4 is a block diagram illustrating grouping of devices capable ofexecuting a mission in the integrated decision making and communicationsystem of FIG. 1, according to an embodiment herein;

FIG. 5 is a block diagram illustrating selection of a group of devicesto execute a mission based on a manner of utilizing available resourcesin the integrated decision making and communication system of FIG. 1,according to an embodiment herein;

FIG. 6 is a block diagram illustrating a first device transmitting aproposal to other devices in the integrated decision making andcommunication system of FIG. 1, according to an embodiment herein;

FIG. 7A is a block diagram illustrating a coalition of UAVs grouped toperform a mission, according to an embodiment herein;

FIG. 7B is state diagram illustrating a coalition formation process,according to an embodiment herein;

FIG. 8A is a block diagram illustrating a system for executing amission, according to an embodiment herein;

FIG. 8B is a block diagram illustrating a system for communicatingbetween a group of UAVs, according to an embodiment herein;

FIG. 8C is a block diagram illustrating a system for filtering out theselection of UAVs from a group of UAVs based on different factors,according to an embodiment herein;

FIG. 8D is a block diagram illustrating a system for selecting acombination of UAVs to execute a mission and communicating with theselected UAVs, according to an embodiment herein;

FIG. 8E is a block diagram illustrating a system for generating a set ofavailable resources to execute a mission and determining a combinationof UAVs with the resources to execute the mission, according to anembodiment herein;

FIG. 9A is a flow diagram illustrating a method of autonomouslyassessing a cooperative performance of a task, according to anembodiment herein;

FIG. 9B is a flow diagram illustrating a method of selecting acombination of UAVs to perform a task, according to an embodimentherein;

FIG. 9C is a flow diagram illustrating a method of performing a taskwith a selected combination of UAVs, according to an embodiment herein;

FIG. 9D is a flow diagram illustrating a method of selecting acombination of UAVs to perform a task based on different factors,according to an embodiment herein;

FIG. 10 is a graphical representation illustrating experimentallysimulated stable formed coalitions used to complete two identified taskswith two leader and six follower UAVs, according to an embodimentherein;

FIG. 11 is a graphical representation illustrating the efficiency factorfor the coalition formation method provided by the embodiments hereincompared to the case of selecting the closest UAVs; and

FIG. 12 is a graphical representation illustrating the change incooperative credit of UAVs based on their cooperative/selfish behaviorin resource sharing, according to an embodiment herein.

DETAILED DESCRIPTION OF THE INVENTION

Embodiments of the disclosed invention, its various features and theadvantageous details thereof, are explained more fully with reference tothe non-limiting embodiments that are illustrated in the accompanyingdrawings and detailed in the following description. Descriptions ofwell-known components and processing techniques are omitted to notunnecessarily obscure what is being disclosed. Examples may be providedand when so provided are intended merely to facilitate an understandingof the ways in which the invention may be practiced and to furtherenable those of skill in the art to practice its various embodiments.Accordingly, examples should not be construed as limiting the scope ofwhat is disclosed and otherwise claimed.

According to various examples, the embodiments herein provide aleader-follower coalition system in a UAV network that identifies therequired resources necessary to carry out a task; e.g., perform amission. The mission may be any suitable type of mission includingmilitary tasks related to targets, search-and-rescue and surveillance,etc. A leader UAV sends a call out to other UAVs in the network askingthem to form a coalition based on the available resources that the otherUAVs have to assist in completing the task/mission. The leader UAVdetermines which group of other UAVs can most efficiently complete thetask/mission based on comparing the available resources of the otherUAVs and the required resources necessary to complete the task/mission.The leader UAV then selects the group of UAVs that meets the efficiencycriteria.

Referring now to the drawings, and more particularly to FIGS. 1 through12, where similar reference characters denote corresponding featuresconsistently throughout, there are shown exemplary embodiments. In thedrawings, the size and relative sizes of components, layers, and regionsmay be exaggerated for clarity.

In some examples, the various devices and processors described hereinand/or illustrated in the figures may be embodied as hardware-enabledmodules and may be configured as a plurality of overlapping orindependent electronic circuits, devices, and discrete elements packagedonto a circuit board to provide data and signal processing functionalitywithin a computer. An example might be a comparator, inverter, orflip-flop, which could include a plurality of transistors and othersupporting devices and circuit elements. The modules that are configuredwith electronic circuits process computer logic instructions capable ofproviding digital and/or analog signals for performing various functionsas described herein. As used herein, the modules may refer to anycomponent or set of components that perform the functionality attributedto the module, and may include a physical processor to executeprocessor-readable instructions. Additionally, the modules may includethe processor, the processor readable instructions, circuitry, hardware,storage media, other components, and combinations thereof. The variousfunctions can further be embodied and physically saved as any of datastructures, data paths, data objects, data object models, object files,database components. For example, the data objects could be configuredas a digital packet of structured data. The data structures could beconfigured as any of an array, tuple, map, union, variant, set, graph,tree, node, and an object, which may be stored and retrieved by computermemory and may be managed by processors, compilers, and other computerhardware components. The data paths can be configured as part of acomputer CPU that performs operations and calculations as instructed bythe computer logic instructions. The data paths could include digitalelectronic circuits, multipliers, registers, and buses capable ofperforming data processing operations and arithmetic operations (e.g.,Add, Subtract, etc.), bitwise logical operations (AND, OR, XOR, etc.),bit shift operations (e.g., arithmetic, logical, rotate, etc.), complexoperations (e.g., using single clock calculations, sequentialcalculations, iterative calculations, etc.). The data objects may beconfigured as physical locations in computer memory and can be avariable, a data structure, or a function. In the embodiments configuredas relational databases (e.g., such Oracle® relational databases), thedata objects can be configured as a table or column. Otherconfigurations include specialized objects, distributed objects,object-oriented programming objects, and semantic web objects, forexample. The data object models can be configured as an applicationprogramming interface for creating HyperText Markup Language (HTML) andExtensible Markup Language (XML) electronic documents. The models can befurther configured as any of a tree, graph, container, list, map, queue,set, stack, and variations thereof. The data object files are created bycompilers and assemblers and contain generated binary code and data fora source file. The database components can include any of tables,indexes, views, stored procedures, and triggers.

FIG. 1 illustrates an integrated decision making and communicationsystem 10 comprising a memory 15 to store a list of resources 20necessary to execute a mission 25. In some examples, the memory 15 maybe Random Access Memory (RAM), Read-Only Memory (ROM), a cache memory,hard drive storage, flash memory, or other type of storage mechanism,according to an example. The list of resources 20 may include adescription of components, mechanisms, chemicals, or any other type ofitem or material used to perform a task. In this regard, the mission 25may comprise any type of task, and may be of a commercial,non-commercial, or military task such as target detection, datacollection, target tracking and prosecution, imaging, and surveillance.For example, in one setting, the mission 25 may be to provide suppliesthrough aerial drop-off to people living in a remote location, and theresources 20 may include food, water, medicine, building supplies, etc.Accordingly, the memory 15 stores a list of such resources 20 that arenecessary to perform/execute the mission 25. In some examples, themission 25 may be a predefined static task such that the resources 20are also similarly predefined/static; i.e., prior to carrying out themission 25 the resources 20 are set and will not change. However, inother examples the mission 25 may be dynamic and based on real-timeeffects due to other factors (e.g., environment, third-party influence,technical issues, etc.) the mission 25 may change and as such theresources 20 necessary to execute the mission 25 may also change inreal-time. For example, initially the mission 25 may be to supply foodto people in a remote location. However, during the course of performingthe mission 25, an earthquake may take place requiring the delivery ofmedicine and building supplies also. Accordingly, the list of resources20 may change to accommodate the execution of the changing mission 25.

The system 10 comprises a transceiver 30 to send and receive data 35between communicatively linked devices 40 ₁ . . . 40 _(x). As usedherein, the subscript x refers to any positive integer. In an example,the transceiver 30 may be an electronic device, circuit, or module thatis part of a cellular system, and which enables the exchange of data 35from among networked devices; e.g., devices 40 ₁ . . . 40 _(x). The data35 may comprise any type of qualitative and quantitative data capable ofbeing transmitted through wired or wireless transfer protocols, and thedata 35 may be encrypted.

The communication that occurs in the system 10 may occur through wiredcommunication or wirelessly, in some examples, in an integrated networkof the devices 40 ₁ . . . 40 _(x). In some examples, the devices 40 ₁ .. . 40 _(x) may be operatively linked via one or more electroniccommunication links. For example, such electronic communication linksmay be established, at least in part, via a communication network suchas the Internet and/or other communication networks.

The system 10 further comprises a processor 45. In some examples, theprocessor 45 may comprise a central processing unit (CPU) of the one ormore devices 40 ₁ . . . 40 _(x). In other examples the processor 45 maybe a discrete component independent of other processing components inthe one or more devices 40 ₁ . . . 40 _(x). In other examples, theprocessor 45 may be a microprocessor, microcontroller, hardware engine,hardware pipeline, and/or other hardware-enabled device suitable forreceiving, processing, operating, and performing various functionsrequired by the one or more of the devices 40 ₁ . . . 40 _(x).Furthermore, the processor 45 may be configured to execute modules bysoftware, hardware, firmware, or combinations thereof, or othermechanisms for configuring processing capabilities. In some examples,the processor 45 may be programmable and may be updated in real-time. Inaccordance with the embodiments herein, the memory 15, transceiver 30,and processor 45 are contained within any of the devices 40 ₁ . . . 40_(x). In some examples, each of the devices 40 ₁ . . . 40 _(x) comprisetheir own memory 15, transceiver 30, and processor 45 and are eachcapable of performing the functions associated with these mechanisms asdescribed herein.

The processor 45 is configured to identify a set of available resources50 capable of executing the mission 25 based on the data 35 receivedfrom the devices 40 ₁ . . . 40 _(x). For example, the transceiver 30 mayreceive the data 35 from the devices 40 ₁ . . . 40 _(x) which containsall of the collective available resources 50 from the devices 40 ₁ . . .40 _(x). This data 35 is directly or indirectly transmitted to theprocessor 45 either by wired or wireless transmission. The processor 45analyzes the data 35 to determine which of the available resources 50are capable of executing the mission 25. Moreover, the processor 45 isconfigured to compare the list of resources 20 necessary to execute themission 25 from the memory 15 with the set of available resources 50. Inthis regard, the processor 45 retrieves the list of resources 20 fromthe memory 15 and compares it with the set of available resources 50provided by the devices 40 ₁ . . . 40 _(x). Accordingly, the processor45 is to determine the resources 20 that are required to execute themission 25 and compares it with the set of available resources 50 fromthe devices 40 ₁ . . . 40 _(x) to determine whether the devices 40 ₁ . .. 40 _(x) are capable of providing the necessary resources 50 toaccomplish the mission 25. Since the list of resources 20 indicate whatis necessary (e.g., which resources are required) to perform the mission25, the processor 45 compares the data 35 containing the availableresources 50 with the list of resources 20.

As an alternative to the static configuration described above, in anexample of a dynamic or real-time configuration, when the mission 25changes and the list of resources 20 necessary to perform the mission 25also changes, or when the devices 40 ₁ . . . 40 _(x) begin utilizingand/or exhausting the available resources 50 in performing the mission25, then the transceiver 30 may continue to receive data 35 from thedevices 40 ₁ . . . 40 _(x) and transmit the data 35 to the processor 45to allow the processor 45 to compare the changing list of resources 20with the changing set of available resources 50.

Furthermore, in either the static or dynamic configuration, theprocessor 45 is configured to identify a combination of the devices 40 ato execute the mission 25 based on the comparison of the list ofresources 20 necessary to execute the mission 25 and the set ofavailable resources 50. In this regard, the combination of devices 40 amay be any combination of the devices 40 ₁ . . . 40 _(x) based on theability to execute the mission 25 in view of the set of availableresources 50 of the devices 40 ₁ . . . 40 _(x) and the list of resources20 necessary to execute the mission 25. In one example, the combinationof devices 40 a may be only one of the devices 40 ₁ . . . 40 _(x) and inanother example, the combination of devices 40 a may be the entire setof devices 40 ₁ . . . 40 _(x), and in still another example, thecombination of devices 40 a may be only some of the devices 40 ₁ . . .40 _(x).

FIG. 2, with reference to FIG. 1, illustrates that the processor 45instructs the transceiver 30 to send messages 55 to the identifiedcombination of the devices 40 a to execute the mission 25. The messages55 may be electronic messages wirelessly transmitted, which may beencrypted in an example. In an example, the transceiver sends themessages 55 to only the identified combination of devices 40 a in orderto inform the devices 40 a to execute the mission 25. In anotherexample, the transceiver 30 sends the messages 55 to all of the devices40 ₁ . . . 40 _(x) and identifies the combination of devices 40 a toexecute the mission 25. According to an example, only one message 55 istransmitted to the devices 40 ₁ . . . 40 _(x) and/or the combination ofdevices 40 a. According to another example, separate messages 55 aretransmitted to the devices 40 ₁ . . . 40 _(x) and/or the combination ofdevices 40 a.

FIG. 3, with reference to FIGS. 1 and 2, illustrates that the system 10may comprise a first device 40 ₁ containing the memory 15, thetransceiver 30, and the processor 45 to autonomously store, transceive,and process, respectively, the data 35 without control by a centralizedbase station 60. In an example, the first device 40 ₁ may be consideredto be the leader device in the coalition of devices 40 ₁ . . . 40 _(x).Contrary to the solutions of the conventional systems, the embodimentsherein do not rely on the control of a centralized base station 60. Acentralized base station 60 may exist in the system 10; however, thecentralized base station 60 is not utilized to control the storage bythe memory 15, transceiving by the transceiver 30, and/or the processingby the processor 45. Accordingly, the functionality of the first device40 ₁ is autonomous in terms of storing the list of resources 20necessary to execute a mission 25, sending and receiving data 35 betweenthe communicatively linked devices 40 ₁ . . . 40 _(x), identifying theset of available resources 50 capable of executing the mission 25 basedon the data 35 received from the devices 40 ₁ . . . 40 _(x), comparingthe list of resources 20 necessary to execute the mission 25 from thememory 15 with the set of available resources 50, and identifying acombination of the devices 40 a to execute the mission 25 based on thecomparison of the list of resources 20 necessary to execute the mission25 and the set of available resources 50.

The system 10 may comprise a plurality of other functionally similardevices 40 ₂ . . . 40 _(x) communicatively linked to the first device 40₁. In this regard, the devices 40 ₂ . . . 40 _(x) are functionallysimilar to the first device 40 ₁. Additionally, the other functionallysimilar devices 40 ₂ . . . 40 _(x) may each comprise a similar memory15, transceiver 30, and processor 45 as the first device 40 ₁.Accordingly, any of the devices 40 ₁ . . . 40 _(x) may be considered asthe leader device in the coalition of the devices 40 ₁ . . . 40 _(x).The first device 40 ₁ together with the other functionally similardevices 40 ₂ . . . 40 _(x) may collectively form the coalition ofdevices 40 ₁ . . . 40 _(x) such that each of the devices 40 ₁ . . . 40_(x) may be communicatively linked with each other. As such,communicatively linking the devices 40 ₁ . . . 40 _(x) may include wiredor wireless linking of the devices 40 ₁ . . . 40 _(x), and may furtherinclude direct or indirect connections therebetween. Regardless, of thetype of connection between the devices 40 ₁ . . . 40 _(x), the devices40 ₁ . . . 40 _(x) are configured to send/receive data 35 and messages55 to one another autonomously; i.e., without control by a centralizedbase station 60.

FIG. 4, with reference to FIGS. 1 through 3, illustrates that theprocessor 45 of the first device 40 ₁ groups the plurality of otherfunctionally similar devices 40 ₂ . . . 40 _(x) based on possiblecombinations of devices 40 b comprising resources 20 b capable ofexecuting the mission 25. In this example, the first device 40 ₁ acts asa leader device in the group of devices 40 ₁ . . . 40 _(x), and based onthe data 35 containing the set of available resources 50 received fromthe other functionally similar devices 40 ₂ . . . 40 _(x), the firstdevice 40 ₁ determines which combination of devices 40 b of the otherfunctionally similar devices 40 ₂ . . . 40 _(x) including the firstdevice 40 ₁ contains the resources 20 b capable of executing the mission25. The processor 45 of the first device 40 ₁ may create the possiblecombinations of the devices 40 b by comparing the available resources 20b of each possible combination of the devices 40 b with the list ofresources 20 stored in memory 15 to ensure that the resources 20 b arecapable of executing the mission 25. Accordingly, there may be one ormore possible combinations of devices 40 b capable of executing themission 25.

FIG. 5, with reference to FIGS. 1 through 4, illustrates that theprocessor 45 of the first device 40 ₁ selects the group of the pluralityof other functionally similar devices 40 ₂ . . . 40 _(x) comprising amost efficient utilization of the available resources 50 to execute themission 25 in a predetermined period of time T. In addition to the firstdevice 40 ₁ selecting the group of the plurality of other functionallysimilar devices 40 ₂ . . . 40 _(x), the first device 40 ₁ may includeitself with the group of the plurality of other functionally similardevices 40 ₂ . . . 40 _(x) in order to execute the mission 25. As such,as the leader device in the overall devices 40 ₁ . . . 40 _(x), thefirst device 40 ₁ may autonomously determine whether to include itselfin the combination of devices 40 b that will execute the mission 25. Theprocessor 45 of the first device 40 ₁ may consider any of severalfactors in order to establish the combination of devices 40 b that willexecute the mission 25. In the example of FIG. 5, one such factor is forthe processor 45 to consider which combination of devices 40 b, possiblyincluding the first device 40 ₁, comprises the available resources 50 toperform the mission 25 within the predetermined period of time T, whichmay be initially established by the processor 45 prior to identifyingthe set of available resources 50 capable of executing the mission 25based on the data 35 received from the devices 40 ₁ . . . 40 _(x), orwhich may be established in real-time as the other functionally similardevices 40 ₂ . . . 40 _(x) are in the process of completing the mission25 or based on real-time status changes of any of the other functionallysimilar devices 40 ₂ . . . 40 _(x). Moreover, the processor 45 isconfigured to consider not only which of the combination of devices 40 bcomprises available resources 50 capable of executing the mission 25within the predetermined period of time T, but also factors into thedecision, which combination of devices 40 b will most efficientlyutilize its available resources 50 within the predetermined period oftime T such that some of the other functionally similar devices 40 ₂ . .. 40 _(x) may have functional or performance characteristics (i.e.,speed, accuracy, dependability, strength, etc.) that is relayed in thedata 35 received by the transceiver 30 and transmitted to the processor45 of the first device 40 ₁, and which the processor 45 of the firstdevice 40 ₁ considers in making the selection of the group of devices 40b.

FIG. 6, with reference to FIGS. 1 through 5, illustrates that thetransceiver 30 of the first device 40 ₁ may transmit a proposal 65 tothe plurality of other functionally similar devices 40 ₂ . . . 40 _(x)to request the plurality of other functionally similar devices 40 ₂ . .. 40 _(x) to combine their available resources 50 ₂ . . . 50 _(x) toexecute the mission 25. In this example, the first device 40 ₁ acts asthe leader device in the group of devices 40 ₁ . . . 40 _(x); however,because each of the devices 40 ₁ . . . 40 _(x) are individuallyautonomous, the first device 40 ₁ does not control any of the otherfunctionally similar devices 40 ₂ . . . 40 _(x), and thus the firstdevice 40 ₁ transmits the proposal 65, which may be sent with theelectronic message 55, to the other functionally similar devices 40 ₂ .. . 40 _(x) requesting that one or more of the other functionallysimilar devices 40 ₂ . . . 40 _(x) combine their respective availableresources 50 ₂ . . . 50 _(x) to execute the mission 25 with or withoutthe first device 40 ₁ participating. The transceiver 30 of the firstdevice 40 ₁ may receive responses 66 from the plurality of otherfunctionally similar devices 40 ₂ . . . 40 _(x) of whether to they agreeto utilize their available resources 50 ₂ . . . 50 _(x) to execute themission 25. Accordingly, although one or more of the other functionallysimilar devices 40 ₂ . . . 40 _(x) comprises available resources 50 ₂ .. . 50 _(x), respectively, to execute the mission 25, any of theautonomous other functionally similar devices 40 ₂ . . . 40 _(x) mayelect not to participate in executing the mission 25 and thus may electnot to combine its available resources 50 ₂ . . . 50 _(x), respectively,in order to execute the mission 25. As such, the other functionallysimilar devices 40 ₂ . . . 40 _(x) transmit their respective responses66, which may be in the form of electronic messages sent with the data35, to the first device 40 ₁, in reply to the proposal 65 transmitted bythe first device 40 ₁.

FIG. 7, with reference to FIGS. 1 through 6, illustrates that thedevices 40 ₁ . . . 40 _(x) may comprise a coalition of UAVs 70 ₁ . . .70 _(x) in a network 75 such that UAV 70 ₁ may be considered a leaderUAV and UAVs 70 ₂ . . . 70 _(x) may be considered follower UAVs as itrelates to performing a task or mission 25 on a target 80. There may beone or multiple leader UAVs according to some examples. The target 80may be any physical item, component, structure, animal, person,mechanism, etc., or combinations thereof, that is/are the subject of thetask or mission 25. Furthermore, the target 80 may be multiple targetsand may change in real-time. The UAVs 70 ₁ . . . 70 _(x) may beindividually autonomous; i.e., are not controlled by any other device orany other UAV. The processor 45 may identify a combination of the UAVs(e.g., a coalition) 70 a to assist in executing the mission 25. Themission 25 may involve performing a task such as searching for a target80, etc. In this regard, the combination of UAVs 70 a may include afirst UAV 70 ₁ and any other combination of the other UAVs 70 ₂ . . . 70_(x). Each of the other UAVs 70 ₂ . . . 70 _(x) may comprise a differentavailable resource 50 ₁ . . . 50 _(x) to assist the first UAV 70 ₁ inperforming the mission 25. The first UAV 70 ₁ comprises the processor 45and may communicate with the other UAVs 70 ₂ . . . 70 _(x) bytransmitting electronic messages 85 (e.g., a first electronic message 85₁, a second electronic message 85 ₂, a third electronic message 85 ₃,etc.). The electronic messages 85 may be analogous to the message 55 orproposal 65 described above, or may constitute other messages and/orforms of communication. Any of the UAVs 71 (e.g., UAV 70 ₃, for example)from the group of UAVs 70 ₁ . . . 70 _(x) may be excluded fromconsideration of contributing to execute the mission 25 for anypredetermined reason such as how the UAVs 71 performed in other missions25 a and the status of the UAVs 71 available resources 51 in executingthe mission 25.

FIG. 7A is an example of a commercial scenario of the integrateddecision making and communication system 10 of FIGS. 1 through 6 inwhich the UAVs 70 ₁ . . . 70 _(x) are communicatively connectable in anetwork 75 such that the UAVs 70 ₁ . . . 70 _(x) can belong to differentvendors, and they can collect some sort of monetary benefits forparticipation in each mission 25. In the leader-follower coalitionformation integrated decision making and communication system 10 boththe efficiency in task performance considering the resource constraintsduring the coalition formation from the leader's perspective (e.g.,first UAV 70 ₁), as well as the individual preferences of the followers(e.g., UAVs 70 ₂ . . . 70 _(x)) are considered during the followers'(e.g., UAVs 70 ₂ . . . 70 _(x)) decision making to join the availablecoalitions 70 a. In such a commercial setting, the UAVs 70 ₁ . . . 70_(x) may exhibit selfish behaviors by not utilizing the availableresources 50 ₁ . . . 50 _(x) that they originally committed during thecoalition formation, with the incentive of saving these resources forfuture missions to obtain higher benefits. In the system 10, areputation-based process is considered that keeps the record of theUAVs' 70 ₁ . . . 70 _(x) cooperative behaviors. The cumulative creditsfor UAVs 70 ₁ . . . 70 _(x) are used to identify the trustable UAVsduring the member selection procedure in coalition formation. Thisprocess involves a trade-off scenario for the UAVs 70 ₁ . . . 70 _(x).On one hand, they prefer to avoid resource sharing with others to savetheir limited available resources 50 ₁ . . . 50 _(x), while on the otherhand they need to cooperate with other agents and sustain a goodreputation in order to be selected for future missions.

As used in the nomenclature herein, vectors and matrices are representedby lower-case and upper-case underlined letters, respectively. Thenotations (.)*, (.)^(T) and (.)^(H) demonstrate the conjugate, transposeand conjugate transpose (Hermitian) operations, respectively. The realvalue and imaginary values are shown by

{.} and ℑ{.}. The diagonal matrix A=diag{a} is a matrix whose diagonalelements are the elements of the vector a. Finally, the functionγ_(L,ϵ)(x) for x>0 is defined as:

$\{ {\begin{matrix}x & {{x \leq {1 +}} \in} \\L & {Otherwise}\end{matrix},} $

for a given value of L and small value of ∈, giving:γ_(L,ϵ)(x)=γ_(L,O)(x).

A heterogeneous system of NUAVs, U={U₁, U₂, . . . , U_(N)} isconsidered, where the UAVs (e.g., UAVs 70 ₁ . . . 70 _(x)) can formvarious coalitions 70 a to accomplish the dynamic tasks in the network75. Suppose that the i^(th) UAV is assigned with a capability vectorr=[r_(i) ¹, r_(i) ² . . . , r_(i) ^(N) ^(r) r]^(T) with r_(i) ^(j)≥0,j=1, 2, . . . , N_(r), that identifies the available resources at thisagent assuming that there are N_(r) different types of resources in thenetwork 75. Each resource can be either consumable or non-consumable. Ifthe i^(th) UAV does not have resource j, then r_(i) ^(j)=0, and ifresource j is non-consumable, then r_(i) ^(j)=∞. This vector can varyover time based on the amount of resources the UAVs (e.g., UAVs 70 ₁ . .. 70 _(x)) consume to complete the tasks.

A coalition of users, named S_(k), is a non-empty subset of UAVs,S_(k)⊂U that handles task k. It is assumed that each coalition of UAVs(e.g., coalition of UAVs 70 a) will handle one target 80 at a time.Moreover, a sparse distribution of targets 80 in the environment isconsidered, hence noting the potential distances among the formedcoalitions 70 a, it is assumed that each UAV (e.g., UAVs 70 ₁ . . . 70_(x)) can be only a member of one of these coalitions 70 a at a certaintime. Hence, the coalitions 70 a are non-overlapping, meaning thatS_(k)∩S_(l)=ϕ. The coalition of all UAVs, U is the grand coalition and acoalition that only contains one UAV is called a singleton coalition.Each coalition S_(k) is associated with a vector of available resourceswhich is shown by R_(k)=[R_(k) ¹, R_(k) ², . . . , R_(k) ^(N) ^(r) ]_(T)with R_(k) ^(j)≥0, j=1, 2, . . . , N_(r). For simplicity, one may assumethat the vector R_(k) is additive over the elements' resource vectors inthe coalition; i.e., R_(k)=Σ_(j∈S) _(k) r_(i).

It is assumed that the UAVs 70 ₁ . . . 70 _(x) operate in anunpredictable environment, where targets 80 of various types withdifferent resource requirements appear in random time and locations andmove freely afterwards. Therefore, a base station 60 is not aware of thepotential targets 80. It is assumed that all the UAVs have thecapability of searching for new targets in a limited geographical field,and the target detection is performed by the UAVs independent (i.e.,autonomous) of the base station 60. The procedure of prosecuting atarget 80 is defined as a compound task to be accomplished by acoalition, such that the term task can refer to a set of multiplesub-tasks required to prosecute a target 80. The tasks can differinherently based on the characteristics of their corresponding targets80; hence the number, duration, and location of tasks vary over time.Since each task is associated to a target 80, the terms target and taskmay be used interchangeably herein.

When a UAV (e.g., UAV 70 ₁) detects a task k, it determines the type andamount of the resources 20 required to carry out this task; i.e.,Γ_(k)=[τ_(k) ¹, τ_(k) ², . . . , τ_(k) ^(N) ^(r) ]^(T), where τ_(k) ¹≥0.If this UAV (e.g., UAV 70 ₁) does not have sufficient resources 50 ₁ toperform the detected task, it calls for a coalition formation and servesas the coalition leader. Since the base station 60 has no informationregarding the existing targets 80 in the network 75, the member UAVs(e.g., UAV 70 ₁ . . . 70 _(x)) of each coalition 70 a are configured tolisten to the radio communication of a target 80 and forward it to thebase station 60. In other words, a coalition of UAVs 70 a is supposed torelay the target's message s_(k) with E{s_(k)*s_(k)}=1 to the basestation 60. According to the embodiments herein, first each leader UAV70 ₁ forms an initial coalition of the available UAVs UAV 70 ₁ . . . 70_(x) to maximize the efficiency in task performance for the encounteredtarget 80, and send a request (e.g., proposal 65) to join the coalitionto the selected followers. Then, the follower UAVs 70 ₂ . . . 70 _(x)observe the formed coalitions 70 a by different leaders and decide tojoin the coalition 70 a that benefits them the most. The details of thecoalition formation procedure are further described below. In order fora leader (e.g., UAV 70 ₁) to form a coalition 70 a, it takes intoaccount several factors including i) collecting the required resources20 to perform a task, ii) traveling time of the UAVs 70 ₁ . . . 70 _(x)to the task, and iii) the quality of service (QoS) of target message'scommunication at the base station 60. The goal of this combinationalcomplex optimization problem is to perform the tasks in timely andresource efficient manner. This goal suggests that the members of anewly formed coalitions 70 a should collectively have all the requiredresources 50 to perform the encountered task, while minimally surpassingthe task requirements. Other associated costs to coalition formationinclude a higher chance of UAVs' collisions when having more UAVs in acoalition 70 a, as well as heavier signaling loads for the members toexchange the necessary information.

Another criterion in coalition formation from the perspective of theleaders (e.g., UAV 70 ₁) is the deadline to complete a task. Hence, theleaders (e.g., UAV 70 ₁) take into account the traveling time of theUAVs 70 ₁ . . . 70 _(x) to the task in order to choose the members ofthe coalition 70 a. For this purpose, δ_(i,k) is defined as thetraveling time of the i^(th) member of the coalition to target klocation that should be less than or equal to a preferred thresholdf_(δ)(ρ) as a function of field radius ρ.

One aspect of the embodiments herein is to identify the reliable UAVs(e.g., UAVs in coalition 70 a) in the network 75 by the leaders (e.g.,UAV 70 ₁) to join the coalition(s) and filter out any UAVs (e.g., UAV71) with selfish behavior. Such networks 75 may be prone to suffer fromboth malicious/intruding UAVs as well as selfish ones. Accordingly, thetechnique provided by the embodiments herein prevent potential selfishbehavior of legitimate non-altruistic UAVs in not spending theirresources after joining a coalition to encourage cooperation among them.This is facilitated by defining a cumulative cooperation credit for eachUAV that indicates its cooperative behavior in terms of resource sharingduring task completion over the course of time. On one hand, since theleaders (e.g., UAV 70 ₁) prefer to select the potential followers (e.g.,UAVs 70 ₂ . . . 70 _(x)) with higher credits, the UAVs 70 ₂ . . . 70_(x) are motivated to maintain a good cooperative credit, as furtherdescribed below. On the other hand, the follower UAVs 70 ₂ . . . 70 _(x)similar to other types of cognitive agents may act selfishly and avoidconsuming their resources after joining a coalition 70 a. The incentivebehind this behavior is to save their limited available resources forfuture missions to earn more monetary benefits.

The coalition formation process may occur as follows: First, thecoalition leader (e.g., UAV 70 ₁) broadcasts a proposal 65 to form acoalition 70 a. Then, the UAVs who possess at least one of the requiredresources 20 can provide a response 66 to this request by reportingtheir available resources 50 as well as their current positions. Thismay be referred to as the bid process. The coalition leader (e.g., UAV70 ₁) then evaluates all the bids by assessing the resources offered bythe volunteer UAVs (e.g., UAVs 70 ₂ . . . 70 _(x)), their estimatedarrival time, their cooperative credits as well as the provided QoSs forthe relaying services during the formation process to determine if acoalition 70 a can be formed to complete the encountered task. If acoalition 70 a cannot be formed, the coalition leader (e.g., UAV 70 ₁)informs the UAVs (e.g., UAVs 70 ₂ . . . 70 _(x)), otherwise thecoalition leader (e.g., UAV 70 ₁) provides the selected UAVs in thecoalition 70 a with information about the tasks (e.g., requiredresources and location of the target 80). The UAVs 71 that are notselected by the coalition leader (e.g., UAV 70 ₁) go back to the searchmode. When a UAV receives multiple coalition formation requests, itconsiders two factors of expected increase in its cooperative creditbased on information it received from the leaders (e.g., UAV 70 ₁) aboutrequired resources 20 for the tasks as well as its distance to thetargets 80 in order to decide which coalition 70 a to join. Thecoalition formation process is summarized in FIG. 7B, with reference toFIGS. 1 through 7A. Next, the cooperative communication method to relaya message from the target 80 to the base station 60 is described.

Amplify-and-Forward (AF) is a widely used relaying method incommunication networks due to its simplicity and low-complexity.Beamforming is a technique based on AF relaying, where a set of relaynodes amplify and shift the phase of a transmitter's signal andrebroadcast it such that they add up constructively, while interferingsignals add up destructively. In order to relay the message from thetarget 80 to the ground or base station 60, a beamforming scenario isprovided in which each UAV in the coalition 70 a multiplies the receivedtarget's signal to a complex weight number and rebroadcasts it (e.g., AFrelaying). A common assumption of knowledge of Channel State Information(CSI) in the system 10 is followed in this technique. It is assumed that

${\underset{\_}{h}}_{TU} = \lbrack {h_{{TU}_{0}},h_{{TU}_{1}},\ldots \mspace{11mu},h_{{TU}_{N_{S}}}} \rbrack^{T}$

is the vector of instantaneous channel coefficients between the targetand the coalition members, where N_(S)=|S|−1. Likewise,

${\underset{\_}{h}}_{UB} = {\lbrack {h_{U_{0}B},h_{U_{1}B},\ldots \mspace{11mu},h_{U_{N_{S}}B}} \rbrack^{T}\mspace{14mu} {is}}$

the vector of channel coefficients between the coalition members and thebase station 60. Here, the index 0 represents the leader and the indicesi=1, 2, . . . , N_(S) represent the members of the coalition 70 a. It isassumed that the leader (e.g., UAV 70 ₁) has the knowledge ofinstantaneous reciprocal channel vectors; i.e., h_(TU) and h_(UB)) andis responsible for calculating the optimum beamforming and notifying themembers of its coalition 70 a.

If S_(T) denotes the transmit signal, the vector of the received signalat the coalition can be written as:

x=h _(TU) s _(T) +n  (1)

where n˜

(0, Σ^(1/2)), with diagonal covariance matrix Σ, is an additive zeromean Gaussian noise vector at the coalition members. It is assumed thateach UAV 70 ₁ . . . 70 _(x) is aware of its local noise characteristicsand performs the whitening transformation process before thebeamforming. Hence, the whitened received signal at the UAVs 70 ₁ . . .70 _(x) can be written as:

{tilde over (x)}=Σ ^(−1/2) x=Σ ^(−1/2) h _(TU) s _(T)+Σ^(−1/2) n,  (2)

The i^(th) coalition member, i=0, 1, . . . , N_(S), multiplies thereceived target signal by a complex weight w_(i)* and then relays it.The broadcasted signal by the members of the coalition 70 a can bewritten as follows, assuming w=[w₀, w₁, . . . , w_(N) _(s) ]^(T):

t=W ^(H) {tilde over (x)}=W ^(H)Σ^(−1/2) h _(TU) s _(T) +W ^(H)Σ^(−1/2)n  (3)

where W=diag(w). The received signal at the base station 60 is:

$\begin{matrix}\begin{matrix}{{y_{B} = {{{\underset{\_}{h}}_{UB}^{H}{\underset{\_}{W}}^{H}{{\sum\limits_{\_}}^{{- 1}/2}{{\underset{\_}{h}}_{TU}s_{T}}}} + {{\underset{\_}{h}}_{UB}^{H}{\underset{\_}{W}}^{H}{{\sum\limits_{\_}}^{{- 1}/2}\underset{\_}{n}}} + v}}\;} \\{= {{{\underset{\_}{w}}^{H}{\underset{\_}{H}}_{UB}^{H}{{\sum\limits_{\_}}^{{- 1}/2}{{\underset{\_}{h}}_{TU}s_{T}}}} + {{\underset{\_}{w}}^{H}{\underset{\_}{H}}_{UB}^{H}{{\sum\limits_{\_}}^{{- 1}/2}\underset{\_}{n}}} + v}}\end{matrix} & (4)\end{matrix}$

where, H_(UB)=diag(h_(UB)) and v˜

M(0, σ²) is white Gaussian noise at the base station receiver.Therefore, the Signal-to-Noise Ratio (SNR) at the base station 60 can beexpressed by:

$\begin{matrix}{{{SNR}_{B} = \frac{{\underset{\_}{w}}^{H}{\underset{\_}{kk}}^{H}\underset{\_}{w}}{{{\underset{\_}{w}}^{H}{\underset{\_}{H}}_{UB}^{H}{\underset{\_}{H}}_{UB}\underset{\_}{w}} + \sigma^{2}}},} & (5)\end{matrix}$

where k=H_(UB) ^(H)Σ^(−1/2)h_(TU). Each UAV 70 ₁ . . . 70 _(x)(including the leader UAV 70 ₁) has a limited energy to forward thetarget's signal. Using Equation (3), the power consumption at eachmember of the coalition 70 a for relaying the target's signal can bewritten as:

P _(i,R)=[w ^(H)]_(i)[Σ^(−1/2)]_(i,i)[h _(TU)]_(i)[h _(TU)^(H)]_(i)[Σ^(−1/2)]_(i,i)[w]_(i)+[w ^(H)]_(i)[w]_(i)  (6)

where i=0, 1, 2, . . . , N_(S). It is assumed that the individualtransmission power of UAV i is below a certain threshold, denoted byp_(i) ^(max).

To monitor the cooperative behavior of the UAVs 70 ₁ . . . 70 _(x), acumulative cooperative credit is defined for each UAV 70 ₁ . . . 70 _(x)based on the amount of resources 50 ₁ . . . 50 _(x) that it utilizes fora specific task. It is assumed that the credit of all UAVs 70 ₁ . . . 70_(x) are initialized to an equal initial credit C_(i) ⁽⁰⁾=C>0, i=1, 2, .. . , N. After completing a task, each UAV's credit is calculated usingthe following steps:

First, the credit of UAV i at time n is updated as:

$\begin{matrix}{{\overset{\sim}{C}}_{i}^{(n)} = \{ {\begin{matrix}C_{i}^{({n - 1})} \\C_{i}^{({n - 1})}\end{matrix} + {\Delta \; C_{i}^{(n)}\mspace{14mu} {If}_{Otherwise}^{{{\exists k}|{U_{i} \in S_{k}}},}}} } & (7)\end{matrix}$

Here, the change in credit C_(i) ^((n)) is defined by:

$\begin{matrix}{{\Delta \; C_{i}^{(n)}} = {\frac{\tau_{k}}{\sum_{l \in S_{k}}a_{l}}a_{i}}} & (8)\end{matrix}$

where τ_(k)=Σ_(j=1) ^(N) ^(r) τ_(k) ^(j) represents a value of task kand

$a_{i} = {\sum_{j = 1}^{N_{r}}{\gamma_{1}( \frac{r_{i}^{j}}{\tau_{k}^{j}} )}}$

denotes the effective resource contribution of each UAV 70 ₁ . . . 70_(x).

Second, if all {tilde over (C)}_(i) ^((n)) for I=1, 2, . . . , N areequal, then the process sets C_(i) ^((n))=C to reset the credits.Otherwise:

$\begin{matrix}{C_{i}^{(n)} = {C\frac{{\overset{\sim}{C}}_{i}^{(n)} - {\overset{\sim}{C}}_{\min}}{{\overset{\sim}{C}}_{\max} - {\overset{\sim}{C}}_{\min}}}} & (9)\end{matrix}$

where {tilde over (C)}_(max)=max_(i=1, 2, . . . , N){{tilde over(C)}_(i) ^((n))} and {tilde over(C)}_(min)=min_(i=1, 2, . . . , N){{tilde over (C)}_(i) ^((n))}.

Therefore, the credits in each step are scaled to values within [0,C]range. After the completion of each task, the updated credits arebroadcasted by the coalition leader (e.g., UAV 70 ₁) to be used forfuture coalition formations. For the sake of simplicity, thesuperscripts (n) are dropped hereafter.

In the coalition formation method provided by the embodiments herein,first the leaders (e.g., UAV 70 ₁) select their coalition members (e.g.,UAVs 70 a) among the available candidates (e.g., UAV 70 ₁ . . . 70 _(x))to maximize their corresponding coalition's utility function to enhancethe efficiency in completing their encountered task noting the resourceconstraints. The coalition value for the k^(th) leader is defined insuch a way to: i) assure the existence of required resources 20 tohandle a task, ii) avoid over-spending the resources 50 on a specifictask, iii) guarantee the timely completion of the task, iv) provide therequired quality of communication to relay target's message, and also v)select the reliable UAVs as follows:

$\begin{matrix}{ v \middle| ( S_{k} )  = {{\alpha_{1}{\sum_{i = 1}^{N_{S}}\; C_{i}}} + {\alpha_{2}{\gamma_{({- L})}( \frac{{SNR}_{Thr}}{{SNR}_{S_{k}}} )}} + {\alpha_{3}{\sum_{j = 1}^{N_{r}}\; \gamma}} - {L( \frac{R_{k}^{j}}{\tau_{k}^{j}} )} - {\gamma_{L}( \frac{\max_{i|{U_{i} \in {S_{k}{\{\delta_{i,k}\}}}}}}{f_{\delta}(\rho)} )}}} & (10)\end{matrix}$

for a large enough positive value of L. Here, the design parameters α₁,α₂, and α₃>0 represent the importance of the credit and quality ofcommunication compared to the resource optimization goal for the leaderUAV 70 ₁. Also, SNR_(Thr) represents the minimum required SNR_(S) _(k)to successfully relay the target's message to the base station 60. Themaximum function in the last term of Equation (10) is used to ensurethat even the UAV with the latest time of arrival will be at taskposition in time. The k^(th) leader is a member of the coalition S_(k).

In order to find the optimal coalition which maximizes the leader'sutility function in Equation (10), a search over all 2^(L) ^(k) possiblecoalitions is utilized, where L_(k) denotes the number of potentialfollower UAVs who responded to the proposal of leader k. To avoid suchextensive search, a low complexity merge-and-split technique isprovided. In this method, each leader k separately starts from aninitial state where the set of UAVs who responded to its proposal ispartitioned into L_(k) singleton coalitions. Afterward, in each step,two chosen coalitions S_(ki) and S_(kj) are merged ifv(S_(ki)∪S_(kj))>v(S_(ki))+v(S_(kj)). Here, since the value of thecoalitions 70 a which does not include the leader (e.g., UAV 70 ₁) iszero, the only possible merge happens if a singleton coalition {u} andthe coalition S_(k) which contains the k^(th) leader satisfy thecondition v(S_(k)∪{u})>v(S_(k)). Also, if for a non-singleton coalitionS there exists a partition of two coalitions S_(ki) and S_(kj) such thatv(S=S_(ki)∪S_(kj))<v(S_(ki))+v(S_(kj)), then S splits into S_(ki) andS_(kj), whereby each leader considers itself as a constant member of allcoalitions under evaluation. At each step of the merge-and-splitprocess, the optimum value of the SNR_(B) in the utility function ofEquation (10) for the coalitions may be calculated, as further describedbelow. The merge-and-split process is used to obtain a suboptimalcoalition solution of Equation (10) with lower complexity. When thechanges in coalition values over consequent rounds become below athreshold, the coalition with highest coalition value would be selectedfrom the leader's perspective and the members (e.g., UAVs 70 ₁ . . . 70_(x)) are notified.

The coalition formation process may comprise:

 1: Initializing coalitions   

 Each leader k starts from a partition of the singleton coalitions ofUAVs who responded to its proposal  2: Merge-and-split coalitionformation algorithm by the leader   

 for all available UAVs for this task  3: while Change in coalitionvalues is greater than ∈ do  4:  while S_(i) and S_(j) exist withυ(S_(i) ∪ S_(j)) > υ(S_(i)) + υ(S_(j)) do  5:   Merge S_(i) and S_(j). 6:  end while  7:  while S_(i) and S_(j) exist such that: υ(S = S_(i) ∪S_(j)) < υ(S_(i)) + υ(S_(j)) do  8:   Split S into partitions S_(i) andS_(j).  9:  end while 10:  if  There is a split then 11:   go to 4. 12: end if 13: end while 14: Select the coalition with highest coalitionvalue, notify the UAVs of this selected coalition 15: if All selectedpotential followers said Yes then 16:  Terminate 17: else 18:  Excludethe ones with No response, go to 3 19: end if 20: Selecting the bestformation request from different leaders by each selected follower 21:if Received only one formation request then 22:  Say Yes to that leader23: else 24:  Say Yes to the leader of coalition that maximizes Equation(11), say No to other leaders 25: end if

When the optimal coalitions from the leaders' perspective are formed,the formation requests will be sent out to the selected UAVs. If apotential follower receives multiple requests from different leaders, itprefers to join the coalition which benefits it the most. The utilityfunction of the followers is defined as:

v _(p)(U _(|i) ,S _(k))=ΔC _(i)−α₄δ_(i,k),  (11)

where ΔC_(i) and δ_(i,k) are the expected credit (knowing the requiredresources for the encountered task) and traveling time to task k if userU_(i) joins the coalition S_(k), respectively. Here, α₄ is a designparameter that indicates the importance of the traveling cost comparedto the change in credit.

The coalition formation process includes a series of merge-and-splitcoalition formation steps. After each stage of merge-and-split coalitionformation, the leader (e.g., UAV 70 ₁) sends out the requests 55 andcollects the followers' responses. If all responses are affirmative, thecoalition formation process stops; otherwise, a new merge-and-split isexecuted that keeps the current members with positive responses, andevaluates the new available UAVs.

As such, in order to show the stability of the process, it is sufficientto show that: i) the number of sequential rounds of the process isfinite, and ii) each merge-and-split stage is stable. The first isensured, because at each round, the members with no interest in joiningthe formed coalition 70 a are excluded, and therefore after at mostm_(s) iterations the process stops, where m_(s) is the number of UAVswith a negative response to join a coalition 70 a. The second conditionis also satisfied since the formed coalitions by the leaders (e.g., UAV70 ₁) are D_(hp)-stable. This is due to the fact that the only type ofallowed membership changes are based on single or possibly multiplemerge-and-splits (i.e., a UAV or a group of them are only allowed toleave a partition by means of merges or splitting).

The optimization from the leaders' perspective is to maximize thecoalition value in Equation (10) by searching over the coalitions (i.e.,S) and determine the optimum beamforming scheme; i.e., w to optimizeSNR_(B). For each coalition, the optimal value of Equation (5), SNR_(S)^(opt) can be obtained via the following inner optimization problem:

$\begin{matrix}{{{\max\limits_{\underset{\_}{w}}{{\frac{{\underset{\_}{w}}^{H}{\underset{\_}{kk}}^{H}\underset{\_}{w}}{{{\underset{\_}{w}}^{H}{\underset{\_}{H}}_{UB}^{H}{\underset{\_}{H}}_{UB}\underset{\_}{w}} + \sigma^{2}}\lbrack {\underset{\_}{w}}^{H} \rbrack}_{i}{( {{{{\lbrack \sum^{{- 1}/2} \rbrack_{i,i}\;\lbrack {\underset{\_}{h}}_{TU} \rbrack}_{i}\lbrack {\underset{\_}{h}}_{TU}^{H} \rbrack}_{i}\lbrack \sum^{{- 1}/2} \rbrack}_{i,i} + 1} )\lbrack \underset{\_}{w} \rbrack}_{i}}} \leq P_{i}^{\max}},{i = 0},1,\ldots \mspace{11mu},N_{c}} & (12)\end{matrix}$

A bisection method may be used to solve the fractional QuadraticallyConstrained Quadratic Program (QCQP)s such as the optimization problemin Equation (12) that can be rewritten as:

$\begin{matrix}{{{{\max\limits_{\underset{\_}{w},t}\; {{t\lbrack {\underset{\_}{w}}^{H} \rbrack}_{i}{( {{{{\lbrack \sum^{{- 1}/2} \rbrack_{i,i}\;\lbrack {\underset{\_}{h}}_{TU} \rbrack}_{i}\lbrack {\underset{\_}{h}}_{TU}^{H} \rbrack}_{i}\lbrack \sum^{{- 1}/2} \rbrack}_{i,i} + 1} )\lbrack \underset{\_}{w} \rbrack}_{i}}} \leq P_{i}^{\max}},{i = 0},1,\ldots \mspace{11mu},N_{c}}{{{{\underset{\_}{w}}^{H}{\underset{\_}{kk}}^{H}\underset{\_}{w}} \geq {{t\; {\underset{\_}{w}}^{H}{\underset{\_}{H}}_{UB}^{H}{\underset{\_}{H}}_{UB}\underset{\_}{w}} + {t\; \sigma^{2}}}},}} & (13)\end{matrix}$

where t is an auxiliary variable. In the bisection method, given thevalue of t≥0, the following feasibility check problem is investigated:

Find w[w _(H)]_(i)([Σ^(−1/2)]_(i,i)[h _(TU)]_(i)[h _(TU)^(H)]_(i)[Σ^(−1/2)]_(i,i)+1)[w]_(i) ≤P _(i) ^(max) ,i=0,1, . . . ,N _(c)w ^(H) kk ^(H) w≥tw ^(H) H _(UB) ^(H) H _(UB) w+tσ ².  (14)

If the problem described in Equation (14) is feasible, then the optimalsolution of the problem in Equation (13); i.e., SNR_(S) ^(opt), is lessthan or equal to t; otherwise SNR_(S) ^(opt)≥t. The variable t is uplimited by t^(up) which is obtained in the following Lemma:

Lemma: t^(up)=k^(H)Q⁻¹k is an up limit for objective in optimizationproblem Equation (12). The optimization problem becomes feasible if andonly if the matrix kk^(H)−tH_(UB) ^(H)H_(UB) is non-negative definite.Since the matrix Q=H_(UB) ^(H)H_(UB) is positive semi-definite, thematrix Q^(−1/2)(kk^(H)−tH_(UB)^(H)H_(UB))Q^(−1/2)=Q^(−1/2)kk^(H)Q^(−1/2)−tI must be non-negativedefinite. That results in t≤λ_(max)(Q^(−1/2)kk^(H)Q^(−1/2))=k^(H)Q⁻¹k.

Using the above Lemma and by assuming δ as the absolute precision of thefinal objective function value, the iteration's complexity order of thebisection method can be written as

${O( {\log ( \frac{t^{up}}{\sigma} )} )}.$

The feasibility problem of Equation (14) can be solved using theSemi-Definite Programming (SDP) relaxation method. One point to noticeis whether w^(opt) is an optimal solution to the feasibility problem ofEquation (14), then for any arbitrary real number θ, {tilde over(w)}=w^(opt)e^(j<θ) is also an optimal solution. Therefore, it ispossible to assume that w^(H)H_(UB)Σ^(−1/2)h_(TU) is a non-negative realnumber. By considering this constraint, the feasibility problem ofEquation (14) can be rewritten as the following SOCP problem for a givent≥0:

Find w[w ^(H)]_(i)([Σ^(−1/2)]_(i,i)[h _(TU)]_(i)[h _(TU)^(H)]_(i)[Σ^(−1/2)]_(i,i)+1)[w]_(i) ≤P _(i) ^(max) ,i=0,1, . . . ,N _(c)w ^(H) k≥√{square root over (tw ^(H) H _(UB) ^(H) H _(UB) w+tσ ²)}

{w ^(H) H _(UB) ^(H)Σ^(−1/2) h ^(TU)}≥0I{w ^(H) H _(UB) ^(H)Σ^(−1/2) |h_(TU)}=0.  (15)

The feasibility check problem in Equation (15) can be effectively solvedusing the cvx convex optimization toolbox. By considering the cubiccomplexity order of the SOCP method, the complexity order of thecommunication optimization can be expressed as

$( {{S}^{3}{\log ( \frac{t^{up}}{\delta} )}} ).$

FIGS. 8A through 8E, with reference to FIGS. 1 through 7B, illustratesan example system 100 to identify, by a first UAV 70 ₁, a combination ofUAVs 70 ₁ . . . 70 _(x) to execute a mission 25. Processor 45 mayinclude a central processing unit, microprocessors, microcontroller,hardware engines, and/or other hardware devices suitable for retrievaland execution of computer-executable instructions 115, 120, 125, 130,135, 140, 145, 150, 155, 160, 165, 170, 175, and 180 stored in amachine-readable storage medium 110.

Processor 45 may fetch, decode, and execute computer-executableinstructions 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170,175, and 180 to enable execution of locally-hosted applications forcontrolling action of the first UAV 70 ₁. As an alternative or inaddition to retrieving and executing instructions, processor 45 mayinclude one or more electronic circuits including a number of electroniccomponents for performing the functionality of one or more of theinstructions 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170,175, and 180.

The machine-readable storage medium 110 may be any electronic, magnetic,optical, or other physical storage device that storescomputer-executable instructions 115, 120, 125, 130, 135, 140, 145, 150,155, 160, 165, 170, 175, and 180. Thus, the machine-readable storagemedium 110 may be, for example, Random Access Memory, anElectrically-Erasable Programmable Read-Only Memory, volatile memory,non-volatile memory, flash memory, a storage drive (e.g., a hard drive),a solid-state drive, optical drive, any type of storage disc (e.g., acompact disc, a DVD, etc.), and the like, or a combination thereof. Inone example, the machine-readable storage medium 110 may include anon-transitory computer-readable storage medium.

In an example, the processor 45 of the first UAV 70 ₁ executes thecomputer-executable instructions 115, 120, 125, 130, 135, 140, 145, 150,155, 160, 165, 170, 175, and 180. In FIG. 8A, the determininginstructions 115 may determine resources 20 used to execute a mission25. These resources 20 may be the minimal amount or type of resourcesnecessary to execute the mission 25. Moreover, the mission 25 may be apredefined mission or may be a dynamic mission that changes inreal-time. The generating instructions 120 may generate a set ofavailable resources 50 capable of executing the mission 25 from a groupof UAVs 70 ₁ . . . 70 _(x) in communication with the first UAV 70 ₁. Thecommunication from amongst the group of UAVs 70 ₁ . . . 70 _(x) mayoccur using any type of communication system and through any type ofcommunication network. In an example, the communication between thegroup of UAVs 70 ₁ . . . 70 _(x) may occur directly and without anyintermediary or centralized base station 60. The set of availableresources 50 capable of executing the mission 25 may include anycombination of resources available from the UAVs 70 ₁ . . . 70 _(x)including the first UAV 70 ₁. The comparing instructions 125 may comparethe resources 20 used for executing the mission 25 with the set ofavailable resources 50. The comparison of the resources 20 that arenecessary to execute the mission 25 and the set of available resources50 may occur in real-time, according to an example, as any of theresources 20 and the set of available resources 50 changes due toupdates or changes to the requirements for performing the mission 25,and the status or availability of the available resources 50 due to theexhaustion of the available resources 50 overtime. The identifyinginstructions 130 may identify a combination of the UAVs 70 a to assistin executing the mission 25 based on the comparison of the resources 20used for executing the mission 25 and the set of available resources 50.The combination of the UAVs 70 a may include any suitable combination ofthe UAVs 70 ₁ . . . 70 _(x) including the first UAV 70 ₁.

In FIG. 8B, the identifying instructions 135 may identify the group ofUAVs 70 ₁ . . . 70 _(x), which are available in the coalition and whichare in communication with at least the first UAV 70 ₁. The group of UAVs70 ₁ . . . 70 _(x) may be geographically dispersed in any location(s),and the identifying instructions 135 utilize the inter-communicationbetween the group of UAVs 70 ₁ . . . 70 _(x) to identify the coalitionof UAVs 70 ₁ . . . 70 _(x) that may potentially assist in executing themission 25. Transmitting instructions 140 may transmit a request (e.g.,message 55) to the group of UAVs 70 ₁ . . . 70 _(x) to send to the firstUAV 70 ₁ the available resources 50 for each of the UAVs 70 ₁ . . . 70_(x) in order to generate the set of available resources 50 capable ofexecuting the mission 25. The first UAV 70 ₁ determines what theavailable resources 50 are of the group of UAVs 70 ₁ . . . 70 _(x) toconsider whether the mission 25 can be executed based on the resources20 that are necessary to execute the mission 25. Generating instructions145 may generate the set of available resources 50 capable of executingthe mission 25 based on an assessment of which combination of theavailable resources 50 from the group of UAVs 70 ₁ . . . 70 _(x) bestmaximizes an efficiency in executing the mission 25. According to anexample, the first UAV 70 ₁ may consider many factors in assembling ateam from amongst the group of UAVs 70 ₁ . . . 70 _(x) to execute themission 25, and one such factor may be to consider how efficiently themission 25 may be executed if performed by a particular combination ofavailable resources 50 from a particular combination of the group ofUAVs 70 ₁ . . . 70 _(x).

As shown in FIG. 8C, the filtering instructions 150 may filter out anyUAVs 71 from the group of UAVs 70 ₁ . . . 70 _(x) based on a pastperformance of executing other missions 25 a. In this regard, filteringout may refer to removing the UAVs 71 from consideration for executingthe mission 25. One factor in considering which of the UAVs 71, if any,to remove from consideration (i.e., filtering out) is the performance ofUAVs 70 ₁ . . . 70 _(x) in executing other missions 25 a. Accordingly,if the UAVs 71 did not perform to a predetermined standard on anothermission 25 a or did not execute the mission 25 a, then the filteringinstructions 150 considers this in its analysis. Filtering instructions155 may filter out any UAVs 71 from the group of UAVs 70 ₁ . . . 70 _(x)based on a real-time assessment of a performance of any of the UAVs' 71utilization of its available resources 51 in executing the mission 25.In this regard, the UAVs 71 may be removed from consideration forexecuting the mission 25 if the real-time performance of the UAVs 71 inexecuting the mission 25 is below a predefined standard. In this regard,even if the UAVs 71 were initially selected to execute the mission 25,the real-time performance assessment may result in removal of the UAVs71 from continuing in performing the mission 25. For example, while onroute to performing the mission 25, the UAVs 71 may experience atechnical failure in payload delivery or guidance system malfunction, orany other deterioration or impedance of utilizing its availableresources 51, and thus the filtering instructions 155 may determine thatthese UAVs 71 are no longer practical for executing the mission 25, andthus are removed from consideration (i.e., filtered out) for continuingwith the mission 25. Filtering instructions 160 may filter out any UAVs71 from the group of UAVs 70 ₁ . . . 70 _(x) that decline to offer itsavailable resources 51 in executing the mission 25. Each of the UAVs 70₁ . . . 70 _(x) may be autonomous (i.e., not controlled by any othersystem or device), and therefore any of the UAVs 70 ₁ . . . 70 _(x) maydetermine that it will not execute the mission 25 or provide itsavailable resources 51, and thus such UAVs 71 are removed fromconsideration (i.e., filtered out) for performing the mission 25.

In FIG. 8D, the selecting instructions 165 may select the combination ofthe UAVs 70 a to assist in executing the mission 25. Once the availableresources 50 are compared with the necessary resources 20 to completethe mission 25, the combination of UAVs 70 a that are capable ofcompleting the mission 25 is selected. In an example, the first UAV 70 ₁performs the selection process. The transmitting instructions 170 maytransmit a coalition message 55 to the selected UAVs 70 a to execute themission 25. In an example, the first UAV 70 ₁ transmits the message 55to the selected UAVs 70 a either separate to each of the UAVs 70 a orcollectively to the group of UAVs 70 ₁ . . . 70 _(x).

As indicated in FIG. 8E, the generating instructions 175 may generatethe set of available resources 50 capable of executing the mission 25based on assigning a predetermined value to each of the availableresources 50 provided by each of the UAVs 70 ₁ . . . 70 _(x). In thisregard, the predetermined value may be any of a qualitative andquantitative value of importance, necessity, or efficiency with regardto the available resources 50 capable of executing the mission 25. Thispredetermined value is used to select the UAVs 70 a to execute themission 25 by determining which of the group of UAVs 70 ₁ . . . 70 _(x)has the requisite combination of available resources 50 to execute themission 25. The determining instructions 180 may determine whichcombination of UAVs 70 a corresponds to the predetermined value of theavailable resources 50 to complete the mission 25 without overspendingthe available resources 50. This allows for the efficient utilizing ofthe available resources 50 from amongst the group of UAVs 70 ₁ . . . 70_(x). Accordingly, the overspending of the available resources 50 mayinvolve duplicating or overusing the available resources 50 forexecuting the mission 25. In an example, the predetermined value may bethe highest value or the optimum value attributed to the availableresources 50 by the processor 45. In other examples, the predeterminedvalue may be the lowest value or may be set to any suitable threshold orscale.

FIGS. 9A through 9D, with reference to FIGS. 1 through 8E, are flowdiagrams illustrating a method 200 of autonomously assessing acooperative performance of a task (i.e., mission 25). The problem ofcooperative task completion in a network 75 of heterogeneous UAVs 70 ₁ .. . 70 _(x) with constrained individual resources in considered forimplementing the method 200, according to an example. It may be assumedthat a number of targets 80 are distributed in an unknown environment,where no prior information about the targets' time of appearance andlocation is available. It may also be assumed that there is one compoundtask (i.e., mission 25) associated with each target 80. The tasks (i.e.,missions 25) can differ intrinsically based on the characteristics oftheir encountered targets 80 in terms of mobility, speed, position, andthe required resources 20. For example, prosecution of a fighter tank ora long-range missile in a battlefield require different sets ofequipment and resources 20. The objectives of the method 200 include: i)locating the distributed targets 80, ii) identifying their associatedtasks (i.e., missions 25) and required resources 20, and iii) completingthe identified tasks (i.e., missions 25). In order to accomplish theseobjectives, the method 200 forms one or more coalitions of UAVs 70 ausing the above-described coalition formation method, in which eachcoalition will complete a task (i.e., mission 25) associated to onetarget 80. The coalition formation technique provided by the embodimentsherein with a leader-follower structure is configured to ensureproviding adequate resources 50 to complete each encountered task (i.e.,mission 25) while not exceeding, or at the very least, minimallyexceeding the minimum required resources 20. The dynamic coalitionformation technique implemented by method 200 enables the UAVs 70 ₁ . .. 70 _(x) to overcome the limitations of their individual capabilitiessuch as limited payload and computation, and communication resources.The formation of a coalition(s) of UAVs 70 a can also extend thecoverage area in target tracking and surveillance applications comparedto utilizing individual UAVs only. The traveling distances of the UAVs70 ₁ . . . 70 _(x) to the tasks (i.e., missions 25) are also taken intoaccount in forming optimal coalitions of UAVs 70 a to complete the tasks(i.e., missions 25) in a timely manner. Furthermore, in order to getmore detailed information about the identified targets 80 (e.g., anadversary object in a battlefield, etc.), each coalition of UAVs 70 amay forward the broadcast messages by their encountered target 80 to abase station 60 using a beamforming technique.

The method 200 in FIG. 9A comprises operating (205) a first UAV 70 ₁ tosearch for a target 80. The searching may occur by the first UAV 70 ₁moving towards the target 80 or by the first UAV 70 ₁ using signaldetection to locate the target 80 while the first UAV 70 ₁ is in ahovering state of operation. Upon locating the target, the method 200comprises assessing (210) a requirement for the first UAV 70 ₁ toperform the task (i.e., mission 25) relating to the target 80. In thisregard, the task (i.e., mission 25) may require some action related tothe target 80. Next, the method 200 comprises transmitting (215) a firstelectronic message 85 ₁ to other UAVs 70 ₂ . . . 70 _(x) capable ofassisting the first UAV 70 ₁ in performing the task (i.e., mission 25).The first electronic message 85 ₁ may be transmitted directly from thefirst UAV 70 ₁ to the other UAVs 70 ₂ . . . 70 _(x) or through a basestation 60. Furthermore, the first electronic message 85 ₁ may compriseany of wired or wireless communications, and the first electronicmessage 85 ₁ may be encrypted. Thereafter, the method 200 may comprisereceiving (220) at least one second electronic message 85 ₂ from theother UAVs 70 ₂ . . . 70 _(x) identifying available resources 50 fromeach of the other UAVs 70 ₂ . . . 70 _(x) to perform the task (i.e.,mission 25). The second electronic message 85 ₂ may be transmitteddirectly to the first UAV 70 ₁ from the other UAVs 70 ₂ . . . 70 _(x) orthrough a base station 60. Furthermore, the second electronic message 85₂ may comprise any of wired or wireless communications, and the secondelectronic message 85 ₂ may be encrypted. Upon completion of this, themethod 200 comprises determining (225) which combination of the otherUAVs 70 ₂ . . . 70 _(x) is best capable of assisting the first UAV 70 ₁in performing the task (i.e., mission 25) based on a predefinedcriterion. In an example, the first UAV 70 ₁ may not join in performingthe task (i.e., mission 25). According to some examples, the predefinedcriterion may be changed based on the dynamic nature of the requiredtask (i.e., whether the mission 25 changes after determining (225) whichcombination of the other UAVs 70 ₂ . . . 70 _(x) is best capable ofassisting the first UAV 70 ₁ in performing the task, etc.). Otherpredefined criterion are described further below with reference to FIG.9D.

As shown in FIG. 9B, the method 200 may further comprise selecting (230)the combination of the other UAVs 70 ₂ . . . 70 _(x) that is bestcapable of assisting the first UAV 70 ₁ in performing the task (i.e.,mission 25). Accordingly, the coalition formation involves selecting theother UAVs 70 ₂ . . . 70 _(x) to establish the coalition of UAVs 70 a.The method 200 further comprises transmitting (235) a third electronicmessage 85 ₃ to the other UAVs 70 ₂ . . . 70 _(x) requesting theselected combination of the other UAVs 70 ₂ . . . 70 _(x) to assist thefirst UAV 70 ₁ in performing the task (i.e., mission 25). Due to theautonomous configuration of each of the UAVs 70 ₁ . . . 70 _(x), thefirst UAV 70 ₁ transmits the third electronic message 85 ₃ to the otherUAVs 70 ₂ . . . 70 _(x) requesting their assistance in performing thetask (i.e., mission 25). The third electronic message 85 ₃ may betransmitted directly from the first UAV 70 ₁ to the other UAVs 70 ₂ . .. 70 _(x) or through a base station 60. Furthermore, the thirdelectronic message 85 ₃ may comprise any of wired or wirelesscommunications, and the third electronic message 85 ₃ may be encrypted.

As shown in FIG. 9C, the method 200 may further comprise performing(240) the task (i.e., mission 25) using the first UAV 70 ₁ with theselected combination of the other UAVs 70 ₂ . . . 70 _(x). In anotherexample, the coalition or combination of UAVs 70 a performing the task(i.e., mission 25) may or may not include the first UAV 70 ₁. As shownin FIG. 9D, the method 200 may further comprise selecting (245) thecombination of the other UAVs 70 ₂ . . . 70 _(x) based on any of a timeT required to perform the task (i.e., mission 25), resource allocationcapable of being provided by the other UAVs 70 ₂ . . . 70 _(x), a trustfactor associated with the other UAVs 70 ₂ . . . 70 _(x), and alikelihood of performance of the task (i.e., mission 25) based on theavailable resources 50 from each of the other UAVs 70 ₂ . . . 70 _(x).

In this regard, the first UAV 70 ₁ may consider several of thesepredefined criterion, among others, to create the coalition orcombination of UAVs 70 a to perform the task (i.e., mission 25). Thetime T may be set based on a desired time established by the first UAV70 ₁, base station 60, or system programmer associated with the system10. The resource allocation may include the available resources 50 ₂ . .. 50 _(x) associated with the other UAVs 70 ₂ . . . 70 _(x) as well asthe available resources 50 ₁ of the first UAV 70 ₁ and what the optimumresource allocation is amongst the available resources 50 ₁ . . . 50_(x) to satisfy the required or necessary resources 20 withoutoverspending (i.e., overusing, duplication, or otherwise wasting) theavailable resources 50 ₁ . . . 50 _(x) or with only a minimal amount ofoverspending of the available resources 50 ₁ . . . 50 _(x). The trustfactor may involve the reputation of the other UAVs 70 ₂ . . . 70 _(x),which may be based on their respective performance(s) on prior missions25 a, and the extent that the first UAV 70 ₁ can trust that the otherUAVs 70 ₂ . . . 70 _(x) will be able to perform the current task (i.e.,mission 25). The likelihood of performance of the task (i.e., mission)may involve the first UAV 70 ₁ determining that the task (i.e., mission25) will likely be successfully accomplished based on the availableresources 50 ₁ . . . 50 _(x) and a comparison to the required ornecessary resources 20 for performing the task (i.e., mission 25).

Furthermore, each of the other UAVs 70 ₂ . . . 70 _(x) may comprise adifferent available resource 50 ₂ . . . 50 _(x) compared to one anotherto assist the first UAV 70 ₁ in performing the task (i.e., mission 25).Alternatively, the other UAVs 70 ₂ . . . 70 _(x) may comprise the sameor an overlapping set of available resource 50 ₂ . . . 50 _(x) comparedto one another to assist the first UAV 70 ₁ in performing the task(i.e., mission 25). Moreover, the first UAV 70 ₁ may comprise availableresources 50 ₁ that are the same, different, or overlapping to theavailable resources 50 ₂ . . . 50 _(x) associated with any of the otherUAVs 70 ₂ . . . 70 _(x).

In order to validate the performance of the method 200, a simulatedexperiment of two leader (i.e., two UAVs 70 ₁) and six follower (i.e.,six other UAVs 70 ₂ . . . 70 _(x)) is considered. The UAVs 70 ₁ . . . 70_(x) are uniformly located in a

³ region. It is assumed that the leaders (UAVs 70 ₁) do not carry outthe required resources 20 to perform the identified tasks (i.e.,missions 25) individually and they call out to form coalitions of UAVs70 a. Five types of resources are considered and the amount of eachresource at each UAV 70 ₁ . . . 70 _(x) as well as the requiredresources 20 for each task (i.e., mission 25) are generated randomly.The traveling time is generated proportional to the distance of the UAVs70 ₁ . . . 70 _(x) to the corresponding target 80. All communicationchannels are generated by zero mean Gaussian variables with varianceproportional to the inverse distance between the correspondingtransmitter and receiver antennas.

FIG. 10, with reference to FIGS. 1 through 9D, demonstrates thepositions of UAVs 70 ₁ . . . 70 _(x) in the network 75 and the formedcoalitions of UAVs 70 a in a numerical experiment. For this example, thestable formed coalitions are as S₁={U₁, U₃, U₅, U₆} and S₂={U₂, U₄, U₇,U₈}, where coalitions S₁ and S₂ complete the tasks 1 and 2,respectively. In FIG. 10, the UAVs are shown by a notation ofS_(i){U_(j)} that refers to the UAV number j is in coalition S_(i). Themethod 200 is considered for different scenarios, where the stablecoalitions are formed after a few rounds. The available resources incoalition S₁ provided by each UAV as well as the required resources tocomplete T₁ are listed in FIG. 10.

Table 1 shows the comparison of available resources in coalition S₁versus the required resources to complete the task encountered by thiscoalition. As shown in Table 1, the summation of available resources incoalition S₁ are higher than the required resources for task 1 whichguarantees that this task can be completed by the members of coalitionS₁. Moreover, to evaluate the efficiency of the coalition formationmethod provided by the embodiments herein in a resource constraintnetwork, an Efficiency Factor is defined as E.F.=Σ_(j=1) ^(N) ^(r){Σ_(i∈S) ₁ r_(i) ^(j)/τ₁ ^(j)}/N_(r). This factor evaluates theperformance of the formed coalitions in terms of resource allocationefficiency, where the closer value of E.F. to 1 means the more efficientthe method is in terms of not overspending the resources for aparticular task. In an example, the average E.F. value is 1.11, which isfairly close to 1.

TABLE 1 Comparison of the Available Resources and the Required Resourcesin Coalition S₁ Available resources in S₁$\sum\limits_{i \in S_{1}}\; r_{i}^{j}$ Required resources for T₁ (τ₁^(j)) ${\sum\limits_{i \in S_{1}}^{\;}\; r_{i}^{j}} \geq \tau_{1}^{j}$$\sum\limits_{i \in S_{1}}\; \frac{r_{i}^{j}}{\tau_{1}^{j}}$ resource1: 2.37 2.37 ✓ 1.00 resource 2: 2.87 2.78 ✓ 1.03 resource 3: 2.90 2.51 ✓1.16 resource 4: 1.36 1.33 ✓ 1.02 resource 5: 1.53 1.15 ✓ 1.33 E.F. =Σj=1Nr {Σi∈S1rij/τ1j}/Nr = 1.11

In FIG. 11, with reference to FIGS. 1 through 10, the efficiency factorof the method 200 after forming stable coalitions is compared to thescenario in which the closest UAVs are assigned to the targets withoutconsidering the resources offered by these UAVs. As shown in FIG. 11,method 200 outperforms the case of distance-based UAVs selection fordifferent system settings over the course of time.

FIG. 12, with reference to FIGS. 1 through 11, evaluates the performanceof the coalition formation method 200 in identifying the potentialselfish UAVs by showing the change in cooperative credit of sixfollower-UAVs over time. The credits are normalized to be in the rangeof [0, 1]. In this scenario, it is assumed that UAVs, U₅ and U₆ areselfish in the sense that they do not consume the resources theyinitially committed after joining a coalition. As seen in FIG. 12, thecredits of these selfish UAVs, C₅ and C₆ significantly decrease overtime, meaning that these selfish users will not be selected by theleaders in the next rounds of coalition formation. Moreover, otherfactors including changes in the UAVs' location and the dynamic natureof the task requirements can also play a role in small variations inagents' credits that may result in credit reduction for trustableagents. The slight reduction of credits of U₃ and U₄ is an example ofthis.

The leader-follower coalition formation method 200 may be utilized fordistributed task allocation and optimizing the cooperative communicationbetween the detected targets 80 and the base station 60 in aheterogeneous network 75 of UAVs 70 ₁ . . . 70 _(x). A reputation-basedapproach is provided to monitor the cooperative behavior of UAVs 70 ₁ .. . 70 _(x) and filter out the selfish UAVs 71 who have not accumulatedsufficient collaboration credits. The systems 10, 100 and method 200enable optimizing several factors including the timely completion of thetasks and preserving the available resources 50 in the network 75 fromthe perspective(s) of the leader(s) (i.e., device 40 ₁ and/or UAV 70 ₁),while it benefits follower devices 40 ₂ . . . 40 _(x) and/or UAVs 70 ₂ .. . 70 _(x) by lowering their travel times to join the coalitions orcombinations of devices 40 a or UAVs 70 a. The simulation results showthe convergence of the method 200 in forming stable coalitions ofdevices 40 a or UAVs 70 a with high resource efficiency factors tocomplete the encountered tasks (i.e. missions 25).

The foregoing description of the specific embodiments will so fullyreveal the general nature of the embodiments herein that others can, byapplying current knowledge, readily modify and/or adapt for variousapplications such specific embodiments without departing from thegeneric concept, and, therefore, such adaptations and modificationsshould and are intended to be comprehended within the meaning and rangeof equivalents of the disclosed embodiments. It is to be understood thatthe phraseology or terminology employed herein is for the purpose ofdescription and not of limitation. Those skilled in the art willrecognize that the embodiments herein can be practiced with modificationwithin the spirit and scope of the appended claims.

What is claimed is:
 1. An integrated decision making and communicationsystem comprising: a memory to store a list of resources necessary toexecute a mission; a transceiver to send and receive data betweencommunicatively linked devices; and a processor to: identify a set ofavailable resources capable of executing the mission based on the datareceived from the devices; compare the list of resources necessary toexecute the mission from the memory with the set of available resources;and identify a combination of the devices to execute the mission basedon the comparison of the list of resources necessary to execute themission and the set of available resources.
 2. The system of claim 1,wherein the processor instructs the transceiver to send messages to theidentified combination of the devices to execute the mission.
 3. Thesystem of claim 1, further comprising: a first device containing thememory, the transceiver, and the processor to autonomously store,transceive, and process the data without control by a centralized basestation; and a plurality of other functionally similar devicescommunicatively linked to the first device.
 4. The system of claim 3,wherein the processor of the first device groups the plurality of otherfunctionally similar devices based on possible combinations of devicescomprising resources capable of executing the mission.
 5. The system ofclaim 4, wherein the processor of the first device selects the group ofthe plurality of other functionally similar devices comprising a mostefficient utilization of the available resources to execute the missionin a predetermined period of time.
 6. The system of claim 3, wherein thetransceiver of the first device: transmits a proposal to the pluralityof other functionally similar devices to request the plurality of otherfunctionally similar devices to combine their available resources toexecute the mission; and receives responses from the plurality of otherfunctionally similar devices of whether to they agree to utilize theiravailable resources to execute the mission.
 7. The system of claim 1,wherein the devices comprise unmanned aerial vehicles (UAVs).
 8. Amachine-readable storage medium comprising computer-executableinstructions that when executed cause a processor of a first unmannedaerial vehicle (UAV) to: determine resources used to execute a mission;generate a set of available resources capable of executing the missionfrom a group of UAVs in communication with the first UAV; compare theresources used for executing the mission with the set of availableresources; and identify a combination of the UAVs to assist in executingthe mission based on the comparison of the resources used for executingthe mission and the set of available resources.
 9. The machine-readablestorage medium of claim 8, wherein the instructions, when executed,further cause the processor to: identify the group of UAVs; and transmita request to the group of UAVs to send to the first UAV the availableresources for each of the UAVs in order to generate the set of availableresources capable of executing the mission.
 10. The machine-readablestorage medium of claim 8, wherein the instructions, when executed,further cause the processor to generate the set of available resourcescapable of executing the mission based on an assessment of whichcombination of the available resources from the group of UAVs bestmaximizes an efficiency in executing the mission.
 11. Themachine-readable storage medium of claim 8, wherein the instructions,when executed, further cause the processor to filter out any UAVs fromthe group of UAVs based on a past performance of executing othermissions.
 12. The machine-readable storage medium of claim 8, whereinthe instructions, when executed, further cause the processor to filterout any UAVs from the group of UAVs based on a real-time assessment of aperformance of any of the UAVs utilization of its available resources inexecuting the mission.
 13. The machine-readable storage medium of claim8, wherein the instructions, when executed, further cause the processorto filter out any UAVs from the group of UAVs that decline to offeravailable resources in executing the mission.
 14. The machine-readablestorage medium of claim 8, wherein the instructions, when executed,further cause the processor to: select the combination of the UAVs toassist in executing the mission; and transmit a coalition message to theselected UAVs to execute the mission.
 15. The machine-readable storagemedium of claim 8, wherein the instructions, when executed, furthercause the processor to: generate the set of available resources capableof executing the mission based on assigning a predetermined value toeach of the available resources provided by each of the UAVs; anddetermine which combination of UAVs corresponds to the predeterminedvalue of the available resources to complete the mission withoutoverspending the available resources.
 16. A method of autonomouslyassessing a cooperative performance of a task, the method comprising:operating a first unmanned aerial vehicle (UAV) to search for a target;upon locating the target, assessing a requirement for the first UAV toperform the task relating to the target; transmitting a first electronicmessage to other UAVs capable of assisting the first UAV in performingthe task; receiving at least one second electronic message from theother UAVs identifying available resources from each of the other UAVsto perform the task; and determining which combination of the other UAVsis best capable of assisting the first UAV in performing the task basedon a predefined criterion.
 17. The method of claim 16, furthercomprising: selecting the combination of the other UAVs that is bestcapable of assisting the first UAV in performing the task; andtransmitting a third electronic message to the other UAVs requesting theselected combination of the other UAVs to assist the first UAV inperforming the task.
 18. The method of claim 17, further comprisingperforming the task using the first UAV with the selected combination ofthe other UAVs.
 19. The method of claim 16, further comprising selectingthe combination of the other UAVs based on any of a time required toperform the task, resource allocation capable of being provided by theother UAVs, a trust factor associated with the other UAVs, and alikelihood of performance of the task based on the available resourcesfrom each of the other UAVs.
 20. The method of claim 16, wherein each ofthe other UAVs comprise a different available resource to assist thefirst UAV in performing the task.